# IMG_PATH = "../input/animals-data/dataset/"
IMG_DATA_PATH = "../data/small_data_oil_for_classification/images/"
TRAIN_DATA_PATH = "../data/small_data_oil_for_classification/train/"
TEST_DATA_PATH = "../data/small_data_oil_for_classification/test/"
NUM_CLASSES = 6
IMG_HEIGHT = 256  # The images are already resized here
IMG_WIDTH = 256  # The images are already resized here

SEED = 42
TRAIN_RATIO = 0.75
VAL_RATIO = 1 - TRAIN_RATIO
SHUFFLE_BUFFER_SIZE = 100

LEARNING_RATE = 1e-3
EPOCHS = 1
TRAIN_BATCH_SIZE = 32  # Let's see, I don't have GPU, Google Colab is best hope
TEST_BATCH_SIZE = 32  # Let's see, I don't have GPU, Google Colab is best hope
FULL_BATCH_SIZE = 32

######################################
# resizer模块相关参数
in_channels= 1       # Number of input channels of resizer (for RGB images it is 3)
out_channels= 1      # Number of output channels of resizer (for RGB images it is 3)
num_kernels = 16      # Same as `n` in paper
num_resblocks = 2     # Same as 'r' in paper
negative_slope = 0.2  # Used by leaky relu
interpolate_mode= "bilinear"  # Passed to torch.nn.functional.interpolate
image_size = 250
resizer_image_size = 224

###### Train and Test time #########

DATA_PATH = "../data/small_data_oil_for_classification/images/"
AUTOENCODER_MODEL_PATH = "baseline_autoencoder.pt"
ENCODER_MODEL_PATH = "../models/convdconv/models-epoch50/deep_encoder.pt"
DECODER_MODEL_PATH = "../models/convdconv/models-epoch50/deep_decoder.pt"
EMBEDDING_PATH = "../models/convdconv/models-epoch50/data_embedding_f.npy"
EMBEDDING_SHAPE = (1, 64, 32, 32)
# TEST_RATIO = 0.2

###### Test time #########
NUM_IMAGES = 10
TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/test/liefeng/0014.png"
